Statistical insights into machine learning analysis can help researchers evaluate model performance and may even provide new physical understanding.

Abstract: In recent years, satellite internet has been widely recognized as a key component of future integrated space-air-ground networks. With advancements in satellite miniaturization and launch ...

Electrochemical impedance spectroscopy (EIS) provides valuable insights into the physical processes within batteries – but how can these measurements directly inform physics-based models? In this ...

Researchers at The University of Texas MD Anderson Cancer Center have performed a comprehensive evaluation of five artificial intelligence (AI) models trained on genomic sequences, known as DNA ...

AI-driven process monitoring enhances the qualification of additively manufactured stainless-steel parts. By analysing high-frequency welding current and voltage signals in both the time and frequency ...

We present the ENV-FIBA macro-micro model framework that can be used to analyze the climate-macro-financial consequences of climate scenarios and related policy counterfactuals. The model consists of ...

We integrated mitochondrial gene expression data with bulk RNA sequencing to identify key mitochondrial genes associated with AML. A total of fourteen machine learning algorithms were employed, ...

Engineers working in the emerging areas of advanced air mobility (AAM) and electric vertical take-off and landing (eVTOL) may be in uncharted skies, literally and figuratively. While transforming ...

Abstract: This paper presents a frequency-dependent cost analysis model for optimizing the economic performance of Low-Frequency AC (LFAC) transmission systems for offshore wind integration. Unlike ...